This table will no longer be updated after 5/30/2024 given the end of the 2023-2024 viral respiratory vaccine season. This table shows the number of CT residents who received an updated 2023-2024 COVID-19 vaccination by week and age group (current age). Only the first dose is counted. CDC recommends that people get at least one dose of this vaccine to protect against serious illness, whether or not they have had a COVID-19 vaccination before. Children and people with moderate to severe immunosuppression might be recommended more than one dose. For more information on COVID-19 vaccination recommendations, click here. • Data are reported weekly on Thursday and include doses administered to Saturday of the previous week (Sunday – Saturday). All data in this report are preliminary. Data from the previous week may be changed because of delays in reporting, deduplication, or correction of errors. • These analyses are based on data reported to CT WiZ which is the immunization information system for CT. CT providers are required by law to report all doses of vaccine administered. CT WiZ also receives records on CT residents vaccinated in other jurisdictions and by federal entities which share data with CT Wiz electronically. Electronic data exchange is being added jurisdiction-by-jurisdiction. Currently, this includes Rhode Island and New York City but not Massachusetts and New York State. Therefore, doses administered to CT residents in neighboring towns in Massachusetts and New York State will not be included. A full list of the jurisdiction with which CT has established electronic data exchange can be seen at the bottom of this page (https://portal.ct.gov/immunization/Knowledge-Base/Articles/Vaccine-Providers/CT-WiZ-for-Vaccine-Providers-and-Training/Query-and-Response-functionality-in-CT-WiZ?language=en_US) • People are included if they have an active jurisdictional status in CT WiZ at the time weekly data are pulled. This excludes people who live out of state, are deceased and a small percentage who have opted out of CT WiZ.

Community specific data reports for vaccine administration results, updated weekly, and data from the Public Health (DPH) COVID Community Impact Survey to help target approaches.

Access available resources below such as data reports, and Public Health Council presentations.

This open dataset shows data on Cambridge residents who have received a COVID-19 vaccine at any location (e.g., mass vaccination site, pharmacy, doctor's office). These data come from the Massachusetts Department of Public Health's weekly report on vaccine doses administered by municipality. The report is released on Thursdays. This open dataset includes data going back several weeks and complements another open dataset called "Cambridge Vaccine Demographics," which shows data for the latest week (https://data.cambridgema.gov/Public-Health/Cambridge-Vaccination-Demographics/66td-u88k)

The Moderna and Pfizer vaccines require two doses administered at least 28 days apart in order to be fully vaccinated. The J&J (Janssen) vaccine requires a single dose in order to be fully vaccinated.

The category "Residents Who Received at Least One Dose" reflects the total number of individuals in the fully and partially vaccinated categories. That is, this category comprises individuals who have received one or both doses of the Moderna/Pfizer vaccine or have received the single dose J&J (Janssen) vaccine.

The category "Fully Vaccinated Residents" comprises individuals who have received both doses of the Moderna/ Pfizer vaccine or the single-dose J&J vaccine.

The category "Partially Vaccinated Residents" comprises individuals who have received only the first dose of the Moderna/Pfizer vaccine.

Source: Weekly COVID-19 Municipality Vaccination Report. Massachusetts releases updated data each Thursday at 5 p.m.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Information about school immunization requirements and data

Results of state-day level difference-in-differences regression estimates of Massachusetts COVID-19 vaccine lottery on the number of vaccinations among adults 18 years or older.

The following dashboards provide data on contagious respiratory viruses, including acute respiratory diseases, COVID-19, influenza (flu), and respiratory syncytial virus (RSV) in Massachusetts. The data presented here can help track trends in respiratory disease and vaccination activity across Massachusetts.

This open dataset shows data on Cambridge residents who have received a COVID-19 vaccine at any location (e.g., mass vaccination site, pharmacy, doctor's office). These data come from the Massachusetts Department of Public Health's weekly report on vaccine doses administered by municipality. The report is released on Thursdays.

The Moderna and Pfizer vaccines require two doses administered at least 28 days apart in order to be fully vaccinated. The J&J (Janssen) vaccine requires a single dose in order to be fully vaccinated.

The category "Residents Who Received at Least One Dose" reflects the total number of individuals in the fully and partially vaccinated categories. That is, this category comprises individuals who have received one or both doses of the Moderna/Pfizer vaccine or have received the single dose J&J (Janssen) vaccine.

The category "Fully Vaccinated Residents" comprises individuals who have received both doses of the Moderna/ Pfizer vaccine or the single-dose J&J vaccine.

The category "Partially Vaccinated Residents" comprises individuals who have received only the first dose of the Moderna/Pfizer vaccine.

Source: Weekly COVID-19 Municipality Vaccination Report. Massachusetts releases updated data each Thursday at 5 p.m.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

The overall goal for this project was to reduce the incidence of COVID-19, hospitalization, and mortality among adults with serious mental illness (SMI) and intellectual disabilities/developmental disabilities (IDD) in congregate living settings (i.e., group homes) in Massachusetts, as well as to reduce COVID-19 incidence among staff who work in these settings. The research team was guided by two comparative effectiveness questions: With the goal of prioritizing and making actionable best practices available as resources, what is the comparative effectiveness of various types and intensities of preventative interventions (e.g., screening, isolation, contact tracing, hand hygiene, physical distancing, use of face masks) in reducing rates of COVID-19, related hospitalizations, and related mortality in this population? With the goal of effectively implementing best practices, what is the most effective implementation strategy to reduce rates of COVID-19 in this population: using tailored best practices (TBP) with SMI/IDD residents and staff of group homes in mind, or general best practices (GBP) from state and federal standard guidelines for all congregate care settings? The specific aims of this study were as follows: Aim 1a. Synthesize existing baseline data collected by 6 state behavioral health agencies on COVID-19 rates, hospitalization, mortality, and use of infection prevention practices. Aim 1b. Collect stakeholder input via surveys and virtual focus groups on staff and resident experiences and on barriers/facilitators to implementing recommended preventative practices. Aims 2a and 2b. Determine the comparative effectiveness of various COVID-19 preventative practices by (Aim 2a) using a validated simulation model to estimate COVID-19 spread in group homes and (Aim 2b) obtaining stakeholder input on prioritizing and defining tailored best practices for implementation. Aim 3. Compare the effectiveness of TBPs with GBPs by using a hybrid effectiveness-implementation cluster randomized controlled trial. Data collected to answer Aims 1 and 2 served as the foundation for designing the Aim 3 trial. Data for the trial were collected in 3-month intervals beginning January 2021 (baseline) until October 2022 (15-month follow-up). Residents and staff were sampled from approximately 400 group homes. Primary implementation outcome measures were COVID-19 vaccination rates and fidelity scores. The primary effectiveness outcome measure was COVID-19 infection. Notes: This collection contains only data from Aim 1a and Aim 3. Throughout the data and documentation, "intellectual and/or developmental disabilities" is abbreviated as both IDD and ID/DD.

Crude and adjusted logistic regression analysis of independent variables of COVID-19 vaccination intention.

Test-to-stay modified quarantine programs implemented in elementary and secondary schools increased participation in in-person learning during the Covid-19 pandemic . Little is known about the impact of other types of testing programs, such as surveillance testing, or immunity and vaccination, on cases of COVID-19 in elementary and secondary school settings. This retrospective cohort study, which was conducted in the state of Massachusetts during the 2021- 22 academic year, found that high vaccination uptake and community immunity acquired via prior infection mitigated COVID-19 cases in elementary and secondary schools. Testing strategies, including surveillance testing programs and test-to-stay modified quarantine programs, for supporting in-person learning were safe and effective but feasibility challenges are important considerations. These data can be used to inform policy about in-school mitigation measures during future respiratory virus pandemics.

This table will no longer be updated after 5/30/2024 given the end of the 2023-2024 viral respiratory vaccine season.

This table shows the number of CT residents who received an updated 2023-2024 COVID-19 vaccination by week and age group (current age). Only the first dose is counted.

CDC recommends that people get at least one dose of this vaccine to protect against serious illness, whether or not they have had a COVID-19 vaccination before. Children and people with moderate to severe immunosuppression might be recommended more than one dose. For more information on COVID-19 vaccination recommendations, click here.
• Data are reported weekly on Thursday and include doses administered to Saturday of the previous week (Sunday – Saturday). All data in this report are preliminary. Data from the previous week may be changed because of delays in reporting, deduplication, or correction of errors.
• These analyses are based on data reported to CT WiZ which is the immunization information system for CT. CT providers are required by law to report all doses of vaccine administered. CT WiZ also receives records on CT residents vaccinated in other jurisdictions and by federal entities which share data with CT Wiz electronically. Electronic data exchange is being added jurisdiction-by-jurisdiction. Currently, this includes Rhode Island and New York City but not Massachusetts and New York State. Therefore, doses administered to CT residents in neighboring towns in Massachusetts and New York State will not be included. A full list of the jurisdiction with which CT has established electronic data exchange can be seen at the bottom of this page (https://portal.ct.gov/immunization/Knowledge-Base/Articles/Vaccine-Providers/CT-WiZ-for-Vaccine-Providers-and-Training/Query-and-Response-functionality-in-CT-WiZ?language=en_US)
• People are included if they have an active jurisdictional status in CT WiZ at the time weekly data are pulled. This excludes people who live out of state, are deceased and a small percentage who have opted out of CT WiZ.

The COVID-19 dashboard includes data on city/town COVID-19 activity, confirmed and probable cases of COVID-19, confirmed and probable deaths related to COVID-19, and the demographic characteristics of cases and deaths.

BackgroundThe Latin American and Caribbean (LAC) region has been one of the regions most affected by the COVID-19 pandemic, with countries presenting some of the highest numbers of cases and deaths from this disease in the world. Despite this, vaccination intention is not homogeneous in the region, and no study has evaluated the influence of the mass media on vaccination intention. The objective of this study was to evaluate the association between the use of mass media to learn about COVID-19 and the non-intention of vaccination against COVID-19 in LAC countries.MethodsAn analysis of secondary data from a Massachusetts Institute of Technology (MIT) survey was conducted in collaboration with Facebook on people's beliefs, behaviors, and norms regarding COVID-19. Crude and adjusted prevalence ratios (aPR) with their respective 95% confidence intervals (95%CI) were calculated to evaluate the association between the use of mass media and non-vaccination intention using generalized linear models of the Poisson family with logarithmic link.ResultsA total of 350,322 Facebook users over the age of 18 from LAC countries were included. 50.0% were men, 28.4% were between 18 and 30 years old, 41.4% had a high school education level, 86.1% lived in the city and 34.4% reported good health condition. The prevalence of using the mass media to learn about COVID-19 was mostly through mixed media (65.8%). The non-intention of vaccination was 10.8%. A higher prevalence of not intending to be vaccinated against COVID-19 was found in those who used traditional media (aPR = 1.36; 95%CI: 1.29–1.44; p < 0.001) and digital media (aPR = 1.70; 95%CI: 1.24–2.33; p = 0.003) compared to those using mixed media.ConclusionWe found an association between the type of mass media used to learn about COVID-19 and the non-intention of vaccination. The use of only traditional or digital information sources were associated with a higher probability of non-intention to vaccinate compared to the use of both sources.

Study participant demographics from 290 study participants enrolled in Massachusetts from April to July 2022 and 286 associated blood samples collected during the study period.

This dataset represents preliminary estimates of cumulative U.S. COVID-19 disease burden for the 2024-2025 period, including illnesses, outpatient visits, hospitalizations, and deaths. The weekly COVID-19-associated burden estimates are preliminary and based on continuously collected surveillance data from patients hospitalized with laboratory-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. The data come from the Coronavirus Disease 2019 (COVID-19)-Associated Hospitalization Surveillance Network (COVID-NET), a surveillance platform that captures data from hospitals that serve about 10% of the U.S. population. Each week CDC estimates a range (i.e., lower estimate and an upper estimate) of COVID-19 -associated burden that have occurred since October 1, 2024.

Note: Data are preliminary and subject to change as more data become available. Rates for recent COVID-19-associated hospital admissions are subject to reporting delays; as new data are received each week, previous rates are updated accordingly.

References

1. Reed C, Chaves SS, Daily Kirley P, et al. Estimating influenza disease burden from population-based surveillance data in the United States. PLoS One. 2015;10(3):e0118369. https://doi.org/10.1371/journal.pone.0118369 
2. Rolfes, MA, Foppa, IM, Garg, S, et al. Annual estimates of the burden of seasonal influenza in the United States: A tool for strengthening influenza surveillance and preparedness. Influenza Other Respi Viruses. 2018; 12: 132– 137. https://doi.org/10.1111/irv.12486
3. Tokars JI, Rolfes MA, Foppa IM, Reed C. An evaluation and update of methods for estimating the number of influenza cases averted by vaccination in the United States. Vaccine. 2018;36(48):7331-7337. doi:10.1016/j.vaccine.2018.10.026 
4. Collier SA, Deng L, Adam EA, Benedict KM, Beshearse EM, Blackstock AJ, Bruce BB, Derado G, Edens C, Fullerton KE, Gargano JW, Geissler AL, Hall AJ, Havelaar AH, Hill VR, Hoekstra RM, Reddy SC, Scallan E, Stokes EK, Yoder JS, Beach MJ. Estimate of Burden and Direct Healthcare Cost of Infectious Waterborne Disease in the United States. Emerg Infect Dis. 2021 Jan;27(1):140-149. doi: 10.3201/eid2701.190676. PMID: 33350905; PMCID: PMC7774540.
5. Reed C, Kim IK, Singleton JA,  et al. Estimated influenza illnesses and hospitalizations averted by vaccination–United States, 2013-14 influenza season. MMWR Morb Mortal Wkly Rep. 2014 Dec 12;63(49):1151-4. https://www.cdc.gov/mmwr/preview/mmwrhtml/mm6349a2.htm 
6. Reed C, Angulo FJ, Swerdlow DL, et al. Estimates of the Prevalence of Pandemic (H1N1) 2009, United States, April–July 2009. Emerg Infect Dis. 2009;15(12):2004-2007. https://dx.doi.org/10.3201/eid1512.091413  
7. Devine O, Pham H, Gunnels B, et al. Extrapolating Sentinel Surveillance Information to Estimate National COVID-19 Hospital Admission Rates: A Bayesian Modeling Approach. Influenza and Other Respiratory Viruses. https://onlinelibrary.wiley.com/doi/10.1111/irv.70026. Volume18, Issue10. October 2024.
8. https://www.cdc.gov/covid/php/covid-net/index.html">COVID-NET | COVID-19 | CDC 
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10. https://academic.oup.com/pnasnexus/article/1/3/pgac079/6604394?login=false">Excess natural-cause deaths in California by cause and setting: March 2020 through February 2021 | PNAS Nexus | Oxford Academic (oup.com)
11. Kruschke, J. K. 2011. Doing Bayesian data analysis: a tutorial with R and BUGS. Elsevier, Amsterdam, Section 3.3.5.

BackgroundVaccine clinical trials should strive to recruit a racially, socioeconomically, and ethnically diverse range of participants to ensure appropriate representation that matches population characteristics. Yet, full inclusion in research is often limited.MethodsA single-center retrospective study was conducted of adults enrolled at Brigham and Women’s Hospital (Boston, MA) between July 2020 and December 2021. Demographic characteristics, including age, race, ethnicity, ZIP code, and sex assigned at birth, were analyzed from both HIV and COVID-19 vaccine trials during the study period, acknowledging the limitations to representation under these parameters. We compared the educational attainment of vaccine trial participants to residents of the Massachusetts metropolitan area, geocoded participants’ addresses to their census block group, and linked them to reported median household income levels from publicly available data for 2020. Frequency and quartile analyses were carried out, and spatial analyses were performed using ArcGIS Online web-based mapping software (Esri).ResultsA total of 1030 participants from four COVID-19 vaccine trials (n = 916 participants) and six HIV vaccine trials (n = 114 participants) were included in the analysis. The median age was 49 years (IQR 33–63) and 28 years (IQR 24–34) for the COVID-19 and HIV vaccine trials, respectively. Participants identifying as White were the majority group represented for both the COVID-19 (n = 598, 65.3%) and HIV vaccine trials (n = 83, 72.8%). Fewer than 25% of participants identified as Hispanic or Latin. Based on ZIP code of residence, the median household income for COVID-19 vaccine clinical trial participants (n = 846) was 102,088 USD (IQR = 81,442–126,094). For HIV vaccine clinical trial participants (n = 109), the median household income was 101,266 USD (IQR 75,052–108,832).ConclusionWe described the characteristics of participants enrolled for HIV and COVID-19 vaccine trials at a single center and found similitude in geographical distribution, median incomes, and proportion of underrepresented individuals between the two types of vaccine candidate trials. Further outreach efforts are needed to ensure the inclusion of individuals from lower educational and socioeconomic brackets. In addition, continued and sustained efforts are necessary to ensure inclusion of individuals from diverse racial and ethnic backgrounds.

Over 12 million people in the United States died from all causes between the beginning of January 2020 and August 21, 2023. Over 1.1 million of those deaths were with confirmed or presumed COVID-19.

Vaccine rollout in the United States Finding a safe and effective COVID-19 vaccine was an urgent health priority since the very start of the pandemic. In the United States, the first two vaccines were authorized and recommended for use in December 2020. One has been developed by Massachusetts-based biotech company Moderna, and the number of Moderna COVID-19 vaccines administered in the U.S. was over 250 million. Moderna has also said that its vaccine is effective against the coronavirus variants first identified in the UK and South Africa.

According to a forecast as of March, BioNTech's and Pfizer's vaccine against COVID-19 could generate sales revenues of nearly 22 billion U.S. dollars during 2021. The BioNTech/Pfizer vaccine was the first COVID-19 vaccine to be widely approved and used. German biotech company BioNTech saw a 156 percent growth in its shares in the last 12 months as of March 2021.

Will Moderna be the big winner? Moderna is expected to be the company with the largest sales revenues from a COVID-19 vaccine. Forecasts predict that the company will make around 43 billion U.S. dollars in sales through its vaccine. Interestingly, Moderna was established in 2010 and had never made profit before the pandemic. Thus, the development of the covid vaccine based on the latest mRNA technology will mark a definitive breakthrough for the Massachusetts-based biotech company. Moderna received significant funding through taxpayer money as well as help in research and development from the National Institutes of Health.

Vaccine pricing in a pandemic Drug pricing is always a big issue and this was also the case with COVID-19 vaccines. While some companies, like AstraZeneca, stated early on that prices for the vaccine will be on a non-profit base at least as long as the pandemic is ongoing, others took a more profit-oriented approach. However, even these companies state that their current prices are low special prices, taking into account urgent public health interests, which normally would be much higher. According to several projections, COVID-19 drugs and vaccines could establish a market worth some 40 billion U.S. dollars annually.

Background: Gender differences in vaccine acceptance among health care workers (HCWs) are well documented, but the extent to which these depend on occupational group membership is less well studied. We aimed to determine vaccine acceptance and reasons of hesitancy among HCWs of respiratory clinics in Germany with respect to gender and occupational group membership. Methods: An online questionnaire for hospital staff of all professional groups was created to assess experiences with and attitudes towards COVID-19 and the available vaccines. Employees of five clinics were surveyed from 15 to 28 March 2021. Results: 962 employees (565 [72%] female) took part in the survey. Overall vaccination acceptance was 72.8%. Nurses and physicians showed greater willingness to be vaccinated than members of other professions (72.8%, 84.5%, 65.8%, respectively; p = 0.006). In multivariate analyses, worries about COVID-19 late effects (odds ratio (OR) 2.86; p < 0.001) and affiliation with physicians (OR 2.20; p = 0.025) were independently associated with the willingness for vaccination, whereas age